1. Technical Field
The present disclosure relates to an imaging system using compressed sensing, and a transmission scheme for image data generated by the imaging system.
2. Description of the Related Art
In recent years, a technique called “compressed sensing” has been developed. Compressed sensing is a technique for compressing images by adding pixel values (electric charge signals) of a plurality of pixels during imaging and reconstructing the image by using sparsity of the image (see, for example, J. Ma, “Improved Iterative Curvelet Thresholding for Compressed Sensing and Measurement”, IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 1, pp. 126-136, 2011). Imaging based on this technique is called “multiple sampling imaging”.
Normally, multiple sampling imaging may cause loss in information amount of an image, resulting in significant degradation of quality of a reconstruction image. However, in compressed sensing, reconstruction processing is performed by using sparsity of an image, and accordingly a reconstruction image having quality equivalent to that of a non-compressed image can be obtained, with an amount of data being reduced by multiple sampling imaging. Here, “sparsity of an image” means that, when an image is projected to a wavelet space or a discrete cosine transform (DCT) space, many coefficient values become almost zero. As an image reconstruction method using sparsity of an image, L0-norm minimization or L1-norm minimization is used in compressed sensing.
In compressed sensing, an amount of data can be reduced by performing simple addition processing before performing processing with an analog-to-digital converter (hereinafter referred to as an ADC) in an imaging device, and thus it is possible to lower the drive frequency of the ADC. Accordingly, lower power consumption, a higher SN ratio, and a reduced communication band can be realized.
The above-mentioned paper of J. Ma discloses a method for applying compressed sensing to an image by using the Improved Iterative Curvelet Thresholding method.
Also, for example, Y. Oike and A. E. Gamal, “A 256×256 CMOS Image Sensor with ΔΣ-Based Single-Shot Compressed Sensing”, IEEE International Solid-State Circuits Conference (ISSCC) Dig. of Tech. Papers, pp. 386-387, 2012 discloses a solid-state imaging device using the concept of compressed sensing. In this solid-state imaging device, a plurality of pixels are respectively connected to a plurality of different lines. The solid-state imaging device sequentially drives a plurality of pixels in a pixel group at timings of shifted phases and thereby reads out signals. With this configuration, a solid-state imaging device including a reduced number of additional circuits, requiring no sample and hold circuit, and capable of preventing degradation of image quality due to increased noise, an increase in area, and decreased speed is obtained.